For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteri...For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.展开更多
A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the...A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.展开更多
针对行人航位推算(Pedestrian Dead Reckoning,PDR)室内定位系统的累计误差问题,提出了一种多维信息感知地标匹配的PDR定位算法(PDR positioning algorithm based Multi-imensional Information Perception Landmark Matching,MIPLM)。...针对行人航位推算(Pedestrian Dead Reckoning,PDR)室内定位系统的累计误差问题,提出了一种多维信息感知地标匹配的PDR定位算法(PDR positioning algorithm based Multi-imensional Information Perception Landmark Matching,MIPLM)。算法利用行人在室内走廊环境下的众包轨迹,并基于突出性路口结构,从位置、航向、影响范围以及WiFi特征指纹等方面构建多维信息感知地标库。给出的自适应地标检测算法,结合航向约束轨迹相似度匹配模型,更新行人位置和航向,避免了本地化匹配过程对空间位置的强依赖性。实验结果表明,相比于其他地标构建及匹配算法,所提算法更好地反映了行人活动与室内空间结构的相关性,且在未知起始位置时,算法能够快速收敛并提供较高的定位精度,对于室内行人连续定位具有较高的应用价值。展开更多
文摘For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.
基金Project(60234030) supported by the National Natural Science Foundation of China
文摘A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.
文摘针对行人航位推算(Pedestrian Dead Reckoning,PDR)室内定位系统的累计误差问题,提出了一种多维信息感知地标匹配的PDR定位算法(PDR positioning algorithm based Multi-imensional Information Perception Landmark Matching,MIPLM)。算法利用行人在室内走廊环境下的众包轨迹,并基于突出性路口结构,从位置、航向、影响范围以及WiFi特征指纹等方面构建多维信息感知地标库。给出的自适应地标检测算法,结合航向约束轨迹相似度匹配模型,更新行人位置和航向,避免了本地化匹配过程对空间位置的强依赖性。实验结果表明,相比于其他地标构建及匹配算法,所提算法更好地反映了行人活动与室内空间结构的相关性,且在未知起始位置时,算法能够快速收敛并提供较高的定位精度,对于室内行人连续定位具有较高的应用价值。
